Geometria dos Embeddings em Recomendação: GNNs e Transformers sob Manifold Learning

• Arthur Andreazza Marinho UNESP https://orcid.org/0009-0005-1708-2131
• Daniel Carlos Guimarães Pedronette UNESP https://orcid.org/0000-0002-2867-4838

DOI: https://doi.org/10.5753/brasnam.2026.22535

Resumo

Foram comparados três paradigmas de recomendação - LightGCN, KGAT e SASRec - sob a perspectiva de manifold learning, com o intuito de verificar como cada arquitetura molda a geometria dos embeddings aprendidos. Foram adotados dois protocolos de avaliação: previsão cronológica do próximo item e recomendação geral top-K, ambos com ranqueamento full-corpus. As métricas de Preservação de Vizinhança (NP@10) e Distorção de Distância Local (LDD@10) são definidas com o grafo de interação de itens (Jaccard) como espaço de referência. Em MovieLens-1M, Yelp2018 e Amazon-Book, o SASRec domina a previsão sequencial, enquanto o LightGCN iguala ou supera o KGAT em recomendação geral, contrariando a hipótese de que grafos de conhecimento aprimoram sistematicamente o desempenho. A análise geométrica esclarece esse resultado.

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